linearmodels.panel.data.PanelData¶

class linearmodels.panel.data.PanelData(x: PanelData | ndarray | DataArray | DataFrame | Series, var_name: str = 'x', convert_dummies: bool = True, drop_first: bool = True, copy: bool = True)[source]¶

Abstraction to handle alternative formats for panel data

Parameters:¶

x: PanelData | ndarray | DataArray | DataFrame | Series¶

Input data

var_name: str = 'x'¶

Variable name to use when naming variables in NumPy arrays or xarray DataArrays

convert_dummies: bool = True¶

Flat indicating whether pandas categoricals or string input data should be converted to dummy variables

drop_first: bool = True¶

Flag indicating to drop first dummy category when converting

copy: bool = True¶

Flag indicating whether to copy the input. Only has an effect when x is a DataFrame

cast : bool

Flag indicating to case the data to double precision.

Notes

Data can be either 2- or 3-dimensional. The three key dimensions are

• nvar - number of variables

• nobs - number of time periods

• nentity - number of entities

All 3-d inputs should be in the form (nvar, nobs, nentity). With one exception, 2-d inputs are treated as (nobs, nentity) so that the input can be treated as-if being (1, nobs, nentity).

If the 2-d input is a pandas DataFrame with a 2-level MultiIndex then the input is treated differently. Index level 0 is assumed ot be entity. Index level 1 is time. The columns are the variables. MultiIndex Series are also accepted and treated as single column MultiIndex DataFrames.

Raises:¶

• TypeError – If the input type is not supported

• ValueError – If the input has the wrong number of dimensions or a MultiIndex DataFrame does not have 2 levels

Methods

copy()	Return a deep copy
count([group])	Count number of observations by entity or time
demean()	Demeans data by either entity or time group
drop(locs)	Drop observations from the panel.
dummies([group, drop_first])	Generate entity or time dummies
first_difference()	Compute first differences of variables
general_demean(groups[, weights])	Multi-way demeaning using only groupby
mean([group, weights])	Compute data mean by either entity or time group

Properties

dataframe	pandas DataFrame view of data
entities	List of entity index names
entity_ids	Get array containing entity group membership information
index	Return the index of the multi-index dataframe view
isnull	Locations with missing observations
ndim	Number of dimensions of panel view of data
nentity	Number of entities
nobs	Number of time observations
nvar	Number of variables
panel	pandas Panel view of data
shape	Shape of panel view of data
time	List of time index names
time_ids	Get array containing time membership information
values2d	NumPy ndarray view of dataframe
values3d	NumPy ndarray view of panel
vars	List of variable names